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You searched for subject:(deep neural network). Showing records 1 – 30 of 193 total matches.

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University of Edinburgh

1. Ge, Mengtian. Statistical Parametric Speech Synthesis Using Deep Neural Network.

Degree: 2013, University of Edinburgh

 In this work, we implement a deep neural network for the text-to-speech system. We have tried different parameter settings for the DNN layers and units,… (more)

Subjects/Keywords: Deep Neural Network; Speech Synthesis

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Ge, M. (2013). Statistical Parametric Speech Synthesis Using Deep Neural Network. (Thesis). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/8658

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Ge, Mengtian. “Statistical Parametric Speech Synthesis Using Deep Neural Network.” 2013. Thesis, University of Edinburgh. Accessed March 21, 2019. http://hdl.handle.net/1842/8658.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Ge, Mengtian. “Statistical Parametric Speech Synthesis Using Deep Neural Network.” 2013. Web. 21 Mar 2019.

Vancouver:

Ge M. Statistical Parametric Speech Synthesis Using Deep Neural Network. [Internet] [Thesis]. University of Edinburgh; 2013. [cited 2019 Mar 21]. Available from: http://hdl.handle.net/1842/8658.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ge M. Statistical Parametric Speech Synthesis Using Deep Neural Network. [Thesis]. University of Edinburgh; 2013. Available from: http://hdl.handle.net/1842/8658

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Texas State University – San Marcos

2. Thanda Setty, Vidya. Speaker Recognition using Deep Neural Networks with reduced Complexity.

Degree: MS, Engineering, 2018, Texas State University – San Marcos

 The goal of this research is to develop a small footprint text-independent speaker recognition system for a closed set of a relatively small number of… (more)

Subjects/Keywords: Speaker recognition; Deep Neural Network

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Thanda Setty, V. (2018). Speaker Recognition using Deep Neural Networks with reduced Complexity. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/7869

Chicago Manual of Style (16th Edition):

Thanda Setty, Vidya. “Speaker Recognition using Deep Neural Networks with reduced Complexity.” 2018. Masters Thesis, Texas State University – San Marcos. Accessed March 21, 2019. https://digital.library.txstate.edu/handle/10877/7869.

MLA Handbook (7th Edition):

Thanda Setty, Vidya. “Speaker Recognition using Deep Neural Networks with reduced Complexity.” 2018. Web. 21 Mar 2019.

Vancouver:

Thanda Setty V. Speaker Recognition using Deep Neural Networks with reduced Complexity. [Internet] [Masters thesis]. Texas State University – San Marcos; 2018. [cited 2019 Mar 21]. Available from: https://digital.library.txstate.edu/handle/10877/7869.

Council of Science Editors:

Thanda Setty V. Speaker Recognition using Deep Neural Networks with reduced Complexity. [Masters Thesis]. Texas State University – San Marcos; 2018. Available from: https://digital.library.txstate.edu/handle/10877/7869


University of Illinois – Urbana-Champaign

3. Yeh, Raymond Alexander. Stable and symmetric convolutional neural network.

Degree: MS, Electrical & Computer Engr, 2016, University of Illinois – Urbana-Champaign

 First we present a proof that convolutional neural networks (CNNs) with max-norm regularization, max-pooling, and Relu non-linearity are stable to additive noise. Second, we explore… (more)

Subjects/Keywords: convolutional neural network; deep learning

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APA (6th Edition):

Yeh, R. A. (2016). Stable and symmetric convolutional neural network. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/92687

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Yeh, Raymond Alexander. “Stable and symmetric convolutional neural network.” 2016. Thesis, University of Illinois – Urbana-Champaign. Accessed March 21, 2019. http://hdl.handle.net/2142/92687.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Yeh, Raymond Alexander. “Stable and symmetric convolutional neural network.” 2016. Web. 21 Mar 2019.

Vancouver:

Yeh RA. Stable and symmetric convolutional neural network. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2016. [cited 2019 Mar 21]. Available from: http://hdl.handle.net/2142/92687.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Yeh RA. Stable and symmetric convolutional neural network. [Thesis]. University of Illinois – Urbana-Champaign; 2016. Available from: http://hdl.handle.net/2142/92687

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


NSYSU

4. Lin, Kun-da. Deep Reinforcement Learning with a Gating Network.

Degree: Master, Electrical Engineering, 2017, NSYSU

 Reinforcement Learning (RL) is a good way to train the robot since it doesn't need an exact model of the environment. All need is to… (more)

Subjects/Keywords: Reinforcement Learning; Deep Reinforcement Learning; Deep Learning; Gating network; Neural network

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Lin, K. (2017). Deep Reinforcement Learning with a Gating Network. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0223117-131536

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Lin, Kun-da. “Deep Reinforcement Learning with a Gating Network.” 2017. Thesis, NSYSU. Accessed March 21, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0223117-131536.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Lin, Kun-da. “Deep Reinforcement Learning with a Gating Network.” 2017. Web. 21 Mar 2019.

Vancouver:

Lin K. Deep Reinforcement Learning with a Gating Network. [Internet] [Thesis]. NSYSU; 2017. [cited 2019 Mar 21]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0223117-131536.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lin K. Deep Reinforcement Learning with a Gating Network. [Thesis]. NSYSU; 2017. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0223117-131536

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Oregon State University

5. Li, Xin. Don't Fool Me : Detecting Adversarial Examples in Deep Networks.

Degree: MS, Electric and Computer Engineering, 2016, Oregon State University

Deep learning has greatly improved visual recognition in recent years. However, recent research has shown that there exist many adversarial examples that can negatively impact… (more)

Subjects/Keywords: Deep Network; Neural networks (Computer science)

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APA (6th Edition):

Li, X. (2016). Don't Fool Me : Detecting Adversarial Examples in Deep Networks. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/59867

Chicago Manual of Style (16th Edition):

Li, Xin. “Don't Fool Me : Detecting Adversarial Examples in Deep Networks.” 2016. Masters Thesis, Oregon State University. Accessed March 21, 2019. http://hdl.handle.net/1957/59867.

MLA Handbook (7th Edition):

Li, Xin. “Don't Fool Me : Detecting Adversarial Examples in Deep Networks.” 2016. Web. 21 Mar 2019.

Vancouver:

Li X. Don't Fool Me : Detecting Adversarial Examples in Deep Networks. [Internet] [Masters thesis]. Oregon State University; 2016. [cited 2019 Mar 21]. Available from: http://hdl.handle.net/1957/59867.

Council of Science Editors:

Li X. Don't Fool Me : Detecting Adversarial Examples in Deep Networks. [Masters Thesis]. Oregon State University; 2016. Available from: http://hdl.handle.net/1957/59867

6. 川西, 誠司. Deep Neural Networkに基づく音声と環境音の同時認識の検討 : Simultaneous Recognition of a Speech and Environmental Sound based on Deep Neural Networks; Deep Neural Network ニ モトズク オンセイ ト カンキョウオン ノ ドウジ ニンシキ ノ ケントウ.

Degree: Nara Institute of Science and Technology / 奈良先端科学技術大学院大学

Subjects/Keywords: Deep Neural Network

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APA (6th Edition):

川西, . (n.d.). Deep Neural Networkに基づく音声と環境音の同時認識の検討 : Simultaneous Recognition of a Speech and Environmental Sound based on Deep Neural Networks; Deep Neural Network ニ モトズク オンセイ ト カンキョウオン ノ ドウジ ニンシキ ノ ケントウ. (Thesis). Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Retrieved from http://hdl.handle.net/10061/10526

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

川西, 誠司. “Deep Neural Networkに基づく音声と環境音の同時認識の検討 : Simultaneous Recognition of a Speech and Environmental Sound based on Deep Neural Networks; Deep Neural Network ニ モトズク オンセイ ト カンキョウオン ノ ドウジ ニンシキ ノ ケントウ.” Thesis, Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Accessed March 21, 2019. http://hdl.handle.net/10061/10526.

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

川西, 誠司. “Deep Neural Networkに基づく音声と環境音の同時認識の検討 : Simultaneous Recognition of a Speech and Environmental Sound based on Deep Neural Networks; Deep Neural Network ニ モトズク オンセイ ト カンキョウオン ノ ドウジ ニンシキ ノ ケントウ.” Web. 21 Mar 2019.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Vancouver:

川西 . Deep Neural Networkに基づく音声と環境音の同時認識の検討 : Simultaneous Recognition of a Speech and Environmental Sound based on Deep Neural Networks; Deep Neural Network ニ モトズク オンセイ ト カンキョウオン ノ ドウジ ニンシキ ノ ケントウ. [Internet] [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; [cited 2019 Mar 21]. Available from: http://hdl.handle.net/10061/10526.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

Council of Science Editors:

川西 . Deep Neural Networkに基づく音声と環境音の同時認識の検討 : Simultaneous Recognition of a Speech and Environmental Sound based on Deep Neural Networks; Deep Neural Network ニ モトズク オンセイ ト カンキョウオン ノ ドウジ ニンシキ ノ ケントウ. [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; Available from: http://hdl.handle.net/10061/10526

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.


University of Toronto

7. Ba, Lei. Adaptive Dropout for Training Deep Neural Networks.

Degree: 2014, University of Toronto

Recently, it was shown that deep neural networks perform very well if the activities of hidden units are regularized during learning, e.g, by randomly dropping… (more)

Subjects/Keywords: deep learning; dropout; neural network; 0800

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Ba, L. (2014). Adaptive Dropout for Training Deep Neural Networks. (Masters Thesis). University of Toronto. Retrieved from http://hdl.handle.net/1807/67873

Chicago Manual of Style (16th Edition):

Ba, Lei. “Adaptive Dropout for Training Deep Neural Networks.” 2014. Masters Thesis, University of Toronto. Accessed March 21, 2019. http://hdl.handle.net/1807/67873.

MLA Handbook (7th Edition):

Ba, Lei. “Adaptive Dropout for Training Deep Neural Networks.” 2014. Web. 21 Mar 2019.

Vancouver:

Ba L. Adaptive Dropout for Training Deep Neural Networks. [Internet] [Masters thesis]. University of Toronto; 2014. [cited 2019 Mar 21]. Available from: http://hdl.handle.net/1807/67873.

Council of Science Editors:

Ba L. Adaptive Dropout for Training Deep Neural Networks. [Masters Thesis]. University of Toronto; 2014. Available from: http://hdl.handle.net/1807/67873


University of Sydney

8. Billingsley, Richard John. Deep Learning for Semantic and Syntactic Structures .

Degree: 2014, University of Sydney

Deep machine learning has enjoyed recent success in vision and speech-to-text tasks, using deep multi-layered neural networks. They have obtained remarkable results particularly where the… (more)

Subjects/Keywords: Deep-learning; parsing; rnn; recursive neural network

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APA (6th Edition):

Billingsley, R. J. (2014). Deep Learning for Semantic and Syntactic Structures . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/12825

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Billingsley, Richard John. “Deep Learning for Semantic and Syntactic Structures .” 2014. Thesis, University of Sydney. Accessed March 21, 2019. http://hdl.handle.net/2123/12825.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Billingsley, Richard John. “Deep Learning for Semantic and Syntactic Structures .” 2014. Web. 21 Mar 2019.

Vancouver:

Billingsley RJ. Deep Learning for Semantic and Syntactic Structures . [Internet] [Thesis]. University of Sydney; 2014. [cited 2019 Mar 21]. Available from: http://hdl.handle.net/2123/12825.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Billingsley RJ. Deep Learning for Semantic and Syntactic Structures . [Thesis]. University of Sydney; 2014. Available from: http://hdl.handle.net/2123/12825

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Sydney

9. Li, Qing. Medical image analysis with neural network and deep learning .

Degree: 2016, University of Sydney

 Thanks to the advance of biomedical imaging systems, large volumes of biomedical image data are generated rapidly. However many doctors still rely on time consuming… (more)

Subjects/Keywords: Medical Imaging; Deep Learning; Artificial Neural Network

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APA (6th Edition):

Li, Q. (2016). Medical image analysis with neural network and deep learning . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/14940

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Li, Qing. “Medical image analysis with neural network and deep learning .” 2016. Thesis, University of Sydney. Accessed March 21, 2019. http://hdl.handle.net/2123/14940.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Li, Qing. “Medical image analysis with neural network and deep learning .” 2016. Web. 21 Mar 2019.

Vancouver:

Li Q. Medical image analysis with neural network and deep learning . [Internet] [Thesis]. University of Sydney; 2016. [cited 2019 Mar 21]. Available from: http://hdl.handle.net/2123/14940.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Li Q. Medical image analysis with neural network and deep learning . [Thesis]. University of Sydney; 2016. Available from: http://hdl.handle.net/2123/14940

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Cambridge

10. Wu, Chunyang. Structured deep neural networks for speech recognition.

Degree: PhD, 2018, University of Cambridge

Deep neural networks (DNNs) and deep learning approaches yield state-of-the-art performance in a range of machine learning tasks, including automatic speech recognition. The multi-layer transformations… (more)

Subjects/Keywords: Deep Learning; Speech Recognition; Neural Network

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wu, C. (2018). Structured deep neural networks for speech recognition. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/276084 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744867

Chicago Manual of Style (16th Edition):

Wu, Chunyang. “Structured deep neural networks for speech recognition.” 2018. Doctoral Dissertation, University of Cambridge. Accessed March 21, 2019. https://www.repository.cam.ac.uk/handle/1810/276084 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744867.

MLA Handbook (7th Edition):

Wu, Chunyang. “Structured deep neural networks for speech recognition.” 2018. Web. 21 Mar 2019.

Vancouver:

Wu C. Structured deep neural networks for speech recognition. [Internet] [Doctoral dissertation]. University of Cambridge; 2018. [cited 2019 Mar 21]. Available from: https://www.repository.cam.ac.uk/handle/1810/276084 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744867.

Council of Science Editors:

Wu C. Structured deep neural networks for speech recognition. [Doctoral Dissertation]. University of Cambridge; 2018. Available from: https://www.repository.cam.ac.uk/handle/1810/276084 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744867


Rochester Institute of Technology

11. Nguyen, Thang Huy. Automatic Video Captioning using Deep Neural Network.

Degree: MS, Computer Engineering, 2017, Rochester Institute of Technology

  Video understanding has become increasingly important as surveillance, social, and informational videos weave themselves into our everyday lives. Video captioning offers a simple way… (more)

Subjects/Keywords: Convolutional neural network; Deep learning; Recurrent neural network; Video captioning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Nguyen, T. H. (2017). Automatic Video Captioning using Deep Neural Network. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/9516

Chicago Manual of Style (16th Edition):

Nguyen, Thang Huy. “Automatic Video Captioning using Deep Neural Network.” 2017. Masters Thesis, Rochester Institute of Technology. Accessed March 21, 2019. https://scholarworks.rit.edu/theses/9516.

MLA Handbook (7th Edition):

Nguyen, Thang Huy. “Automatic Video Captioning using Deep Neural Network.” 2017. Web. 21 Mar 2019.

Vancouver:

Nguyen TH. Automatic Video Captioning using Deep Neural Network. [Internet] [Masters thesis]. Rochester Institute of Technology; 2017. [cited 2019 Mar 21]. Available from: https://scholarworks.rit.edu/theses/9516.

Council of Science Editors:

Nguyen TH. Automatic Video Captioning using Deep Neural Network. [Masters Thesis]. Rochester Institute of Technology; 2017. Available from: https://scholarworks.rit.edu/theses/9516


University of Windsor

12. Akilan, Thangarajah. VIDEO FOREGROUND LOCALIZATION FROM TRADITIONAL METHODS TO DEEP LEARNING.

Degree: PhD, Electrical and Computer Engineering, 2018, University of Windsor

 These days, detection of Visual Attention Regions (VAR), such as moving objects has become an integral part of many Computer Vision applications, viz. pattern recognition,… (more)

Subjects/Keywords: Background subraction; Deep convolutional neural network; Deep learning; Video foreground

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Akilan, T. (2018). VIDEO FOREGROUND LOCALIZATION FROM TRADITIONAL METHODS TO DEEP LEARNING. (Doctoral Dissertation). University of Windsor. Retrieved from https://scholar.uwindsor.ca/etd/7462

Chicago Manual of Style (16th Edition):

Akilan, Thangarajah. “VIDEO FOREGROUND LOCALIZATION FROM TRADITIONAL METHODS TO DEEP LEARNING.” 2018. Doctoral Dissertation, University of Windsor. Accessed March 21, 2019. https://scholar.uwindsor.ca/etd/7462.

MLA Handbook (7th Edition):

Akilan, Thangarajah. “VIDEO FOREGROUND LOCALIZATION FROM TRADITIONAL METHODS TO DEEP LEARNING.” 2018. Web. 21 Mar 2019.

Vancouver:

Akilan T. VIDEO FOREGROUND LOCALIZATION FROM TRADITIONAL METHODS TO DEEP LEARNING. [Internet] [Doctoral dissertation]. University of Windsor; 2018. [cited 2019 Mar 21]. Available from: https://scholar.uwindsor.ca/etd/7462.

Council of Science Editors:

Akilan T. VIDEO FOREGROUND LOCALIZATION FROM TRADITIONAL METHODS TO DEEP LEARNING. [Doctoral Dissertation]. University of Windsor; 2018. Available from: https://scholar.uwindsor.ca/etd/7462


University of Sydney

13. Yuan, Yuchen. Advanced Visual Computing for Image Saliency Detection .

Degree: 2017, University of Sydney

 Saliency detection is a category of computer vision algorithms that aims to filter out the most salient object in a given image. Existing saliency detection… (more)

Subjects/Keywords: saliency detection; image processing; random walks; deep learning; deep neural network; fully convolutional network

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APA (6th Edition):

Yuan, Y. (2017). Advanced Visual Computing for Image Saliency Detection . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/17039

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Yuan, Yuchen. “Advanced Visual Computing for Image Saliency Detection .” 2017. Thesis, University of Sydney. Accessed March 21, 2019. http://hdl.handle.net/2123/17039.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Yuan, Yuchen. “Advanced Visual Computing for Image Saliency Detection .” 2017. Web. 21 Mar 2019.

Vancouver:

Yuan Y. Advanced Visual Computing for Image Saliency Detection . [Internet] [Thesis]. University of Sydney; 2017. [cited 2019 Mar 21]. Available from: http://hdl.handle.net/2123/17039.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Yuan Y. Advanced Visual Computing for Image Saliency Detection . [Thesis]. University of Sydney; 2017. Available from: http://hdl.handle.net/2123/17039

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Illinois – Urbana-Champaign

14. Yan, Zhicheng. Image recognition, semantic segmentation and photo adjustment using deep neural networks.

Degree: PhD, Computer Science, 2016, University of Illinois – Urbana-Champaign

Deep Neural Networks (DNNs) have proven to be effective models for solving various problems in computer vision. Multi-Layer Perceptron Networks, Convolutional Neural Networks and Recurrent… (more)

Subjects/Keywords: Deep Neural Network; Image Recognition; Semantic Segmentation; Photo Adjustment; Convolutional Neural Network; Recurrent Neural Network; Multi-Layer Perceptron Network

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Yan, Z. (2016). Image recognition, semantic segmentation and photo adjustment using deep neural networks. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/90724

Chicago Manual of Style (16th Edition):

Yan, Zhicheng. “Image recognition, semantic segmentation and photo adjustment using deep neural networks.” 2016. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed March 21, 2019. http://hdl.handle.net/2142/90724.

MLA Handbook (7th Edition):

Yan, Zhicheng. “Image recognition, semantic segmentation and photo adjustment using deep neural networks.” 2016. Web. 21 Mar 2019.

Vancouver:

Yan Z. Image recognition, semantic segmentation and photo adjustment using deep neural networks. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2016. [cited 2019 Mar 21]. Available from: http://hdl.handle.net/2142/90724.

Council of Science Editors:

Yan Z. Image recognition, semantic segmentation and photo adjustment using deep neural networks. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2016. Available from: http://hdl.handle.net/2142/90724


Utah State University

15. Tiwari, Astha. A Deep Learning Approach to Recognizing Bees in Video Analysis of Bee Traffic.

Degree: MS, Computer Science, 2018, Utah State University

  Colony Collapse Disorder (CCD) has been a major threat to bee colonies around the world which affects vital human food crop pollination. The decline… (more)

Subjects/Keywords: Computer Vision; Deep Learning; Convolutional Neural Network; Bee Traffic; Artificial Neural Network; Computer Sciences

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Tiwari, A. (2018). A Deep Learning Approach to Recognizing Bees in Video Analysis of Bee Traffic. (Masters Thesis). Utah State University. Retrieved from https://digitalcommons.usu.edu/etd/7076

Chicago Manual of Style (16th Edition):

Tiwari, Astha. “A Deep Learning Approach to Recognizing Bees in Video Analysis of Bee Traffic.” 2018. Masters Thesis, Utah State University. Accessed March 21, 2019. https://digitalcommons.usu.edu/etd/7076.

MLA Handbook (7th Edition):

Tiwari, Astha. “A Deep Learning Approach to Recognizing Bees in Video Analysis of Bee Traffic.” 2018. Web. 21 Mar 2019.

Vancouver:

Tiwari A. A Deep Learning Approach to Recognizing Bees in Video Analysis of Bee Traffic. [Internet] [Masters thesis]. Utah State University; 2018. [cited 2019 Mar 21]. Available from: https://digitalcommons.usu.edu/etd/7076.

Council of Science Editors:

Tiwari A. A Deep Learning Approach to Recognizing Bees in Video Analysis of Bee Traffic. [Masters Thesis]. Utah State University; 2018. Available from: https://digitalcommons.usu.edu/etd/7076


NSYSU

16. Wu, Tung-Han. Combined with Deep Neural Network De-noising Auto Encoder on Noise-Robust Digit Continuous Speech Recognition.

Degree: Master, Computer Science and Engineering, 2017, NSYSU

 In this paper, we combine the deep neural network De-noising Auto Encoder and Gaussian Mixture Model to implement an automatic speech recognition system in the… (more)

Subjects/Keywords: speech recognition; denoising auto encoder; convolutional neural network; deep learning; fully connected neural network

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wu, T. (2017). Combined with Deep Neural Network De-noising Auto Encoder on Noise-Robust Digit Continuous Speech Recognition. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716117-130558

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Wu, Tung-Han. “Combined with Deep Neural Network De-noising Auto Encoder on Noise-Robust Digit Continuous Speech Recognition.” 2017. Thesis, NSYSU. Accessed March 21, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716117-130558.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Wu, Tung-Han. “Combined with Deep Neural Network De-noising Auto Encoder on Noise-Robust Digit Continuous Speech Recognition.” 2017. Web. 21 Mar 2019.

Vancouver:

Wu T. Combined with Deep Neural Network De-noising Auto Encoder on Noise-Robust Digit Continuous Speech Recognition. [Internet] [Thesis]. NSYSU; 2017. [cited 2019 Mar 21]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716117-130558.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wu T. Combined with Deep Neural Network De-noising Auto Encoder on Noise-Robust Digit Continuous Speech Recognition. [Thesis]. NSYSU; 2017. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716117-130558

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Arizona

17. Campbell, Tanner. A Deep Learning Approach to Autonomous Relative Terrain Navigation .

Degree: 2017, University of Arizona

 Autonomous relative terrain navigation is a problem at the forefront of many space missions involving close proximity operations to any target body. With no definitive… (more)

Subjects/Keywords: Artificial Intelligence; Autonomous Navigation; Convolutional Neural Network; Deep Neural Network; Relative Terrain Navigation; Spacecraft GNC

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Campbell, T. (2017). A Deep Learning Approach to Autonomous Relative Terrain Navigation . (Masters Thesis). University of Arizona. Retrieved from http://hdl.handle.net/10150/626706

Chicago Manual of Style (16th Edition):

Campbell, Tanner. “A Deep Learning Approach to Autonomous Relative Terrain Navigation .” 2017. Masters Thesis, University of Arizona. Accessed March 21, 2019. http://hdl.handle.net/10150/626706.

MLA Handbook (7th Edition):

Campbell, Tanner. “A Deep Learning Approach to Autonomous Relative Terrain Navigation .” 2017. Web. 21 Mar 2019.

Vancouver:

Campbell T. A Deep Learning Approach to Autonomous Relative Terrain Navigation . [Internet] [Masters thesis]. University of Arizona; 2017. [cited 2019 Mar 21]. Available from: http://hdl.handle.net/10150/626706.

Council of Science Editors:

Campbell T. A Deep Learning Approach to Autonomous Relative Terrain Navigation . [Masters Thesis]. University of Arizona; 2017. Available from: http://hdl.handle.net/10150/626706


NSYSU

18. Wu, Pei-Hsuan. Architecture Design and Implementation of Deep Neural Network Hardware Accelerators.

Degree: Master, Computer Science and Engineering, 2018, NSYSU

Deep Neural Networks (DNN) widely used in computer vision applications have superior performance in image classification and object detection. However, the huge amount of data… (more)

Subjects/Keywords: CNN hardware accelerator; deep neural network (DNN); convolutional neural network (CNN); machine learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wu, P. (2018). Architecture Design and Implementation of Deep Neural Network Hardware Accelerators. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729118-154714

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Wu, Pei-Hsuan. “Architecture Design and Implementation of Deep Neural Network Hardware Accelerators.” 2018. Thesis, NSYSU. Accessed March 21, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729118-154714.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Wu, Pei-Hsuan. “Architecture Design and Implementation of Deep Neural Network Hardware Accelerators.” 2018. Web. 21 Mar 2019.

Vancouver:

Wu P. Architecture Design and Implementation of Deep Neural Network Hardware Accelerators. [Internet] [Thesis]. NSYSU; 2018. [cited 2019 Mar 21]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729118-154714.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wu P. Architecture Design and Implementation of Deep Neural Network Hardware Accelerators. [Thesis]. NSYSU; 2018. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729118-154714

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Bridgeport

19. Hassan, Abdalraouf. Deep Neural Language Model for Text Classification Based on Convolutional and Recurrent Neural Networks .

Degree: 2018, University of Bridgeport

 The evolution of the social media and the e-commerce sites produces a massive amount of unstructured text data on the internet. Thus, there is a… (more)

Subjects/Keywords: Convolutional neural network; Deep learning; Machine learning; Natural language processing; Recurrent neural network; Sentiment analysis

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Hassan, A. (2018). Deep Neural Language Model for Text Classification Based on Convolutional and Recurrent Neural Networks . (Thesis). University of Bridgeport. Retrieved from https://scholarworks.bridgeport.edu/xmlui/handle/123456789/2274

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Hassan, Abdalraouf. “Deep Neural Language Model for Text Classification Based on Convolutional and Recurrent Neural Networks .” 2018. Thesis, University of Bridgeport. Accessed March 21, 2019. https://scholarworks.bridgeport.edu/xmlui/handle/123456789/2274.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Hassan, Abdalraouf. “Deep Neural Language Model for Text Classification Based on Convolutional and Recurrent Neural Networks .” 2018. Web. 21 Mar 2019.

Vancouver:

Hassan A. Deep Neural Language Model for Text Classification Based on Convolutional and Recurrent Neural Networks . [Internet] [Thesis]. University of Bridgeport; 2018. [cited 2019 Mar 21]. Available from: https://scholarworks.bridgeport.edu/xmlui/handle/123456789/2274.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hassan A. Deep Neural Language Model for Text Classification Based on Convolutional and Recurrent Neural Networks . [Thesis]. University of Bridgeport; 2018. Available from: https://scholarworks.bridgeport.edu/xmlui/handle/123456789/2274

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Penn State University

20. Krishna, Vinayaka. Dense Convolutional Object Detectors for Visual Assistive Systems On Mobile Platform.

Degree: 2018, Penn State University

 There has been increased research effort into developing convolutional neural networks that canrun efficiently on mobile and embedded platforms. Recent work has shown that providing… (more)

Subjects/Keywords: Convolutional Neural Network; DenseNets; ResNet; Deep Learning; Visual Assistance System; Mobile Deep Learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Krishna, V. (2018). Dense Convolutional Object Detectors for Visual Assistive Systems On Mobile Platform. (Thesis). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/15217npk5110

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Krishna, Vinayaka. “Dense Convolutional Object Detectors for Visual Assistive Systems On Mobile Platform.” 2018. Thesis, Penn State University. Accessed March 21, 2019. https://etda.libraries.psu.edu/catalog/15217npk5110.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Krishna, Vinayaka. “Dense Convolutional Object Detectors for Visual Assistive Systems On Mobile Platform.” 2018. Web. 21 Mar 2019.

Vancouver:

Krishna V. Dense Convolutional Object Detectors for Visual Assistive Systems On Mobile Platform. [Internet] [Thesis]. Penn State University; 2018. [cited 2019 Mar 21]. Available from: https://etda.libraries.psu.edu/catalog/15217npk5110.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Krishna V. Dense Convolutional Object Detectors for Visual Assistive Systems On Mobile Platform. [Thesis]. Penn State University; 2018. Available from: https://etda.libraries.psu.edu/catalog/15217npk5110

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


NSYSU

21. Chen, Chien-Hung. Stingray Detection and Recognition of Aerial Videos with Region-based Convolution Neural Network.

Degree: Master, Mechanical and Electro-Mechanical Engineering, 2017, NSYSU

 In recent years, image processing technology has made a major breakthrough because of the appearance of deep learning. Nowadays, many problems that were difficult to… (more)

Subjects/Keywords: Object Detection; Convolution Neural Network; Deep Learning; Machine Vision; Aerial Imaging

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Chen, C. (2017). Stingray Detection and Recognition of Aerial Videos with Region-based Convolution Neural Network. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0708117-234728

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Chen, Chien-Hung. “Stingray Detection and Recognition of Aerial Videos with Region-based Convolution Neural Network.” 2017. Thesis, NSYSU. Accessed March 21, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0708117-234728.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Chen, Chien-Hung. “Stingray Detection and Recognition of Aerial Videos with Region-based Convolution Neural Network.” 2017. Web. 21 Mar 2019.

Vancouver:

Chen C. Stingray Detection and Recognition of Aerial Videos with Region-based Convolution Neural Network. [Internet] [Thesis]. NSYSU; 2017. [cited 2019 Mar 21]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0708117-234728.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chen C. Stingray Detection and Recognition of Aerial Videos with Region-based Convolution Neural Network. [Thesis]. NSYSU; 2017. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0708117-234728

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Northeastern University

22. Corsaro, Matthew. Robotic grasping in cluttered scenes.

Degree: MS, Computer Science Program, 2017, Northeastern University

 Robotic grasping systems that can clear clutter from a surface have many possible applications. One of these grasping systems could be implemented on a mobile… (more)

Subjects/Keywords: convolutional neural network; deep learning; grasp detection; robotics; UR5

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Corsaro, M. (2017). Robotic grasping in cluttered scenes. (Masters Thesis). Northeastern University. Retrieved from http://hdl.handle.net/2047/D20248546

Chicago Manual of Style (16th Edition):

Corsaro, Matthew. “Robotic grasping in cluttered scenes.” 2017. Masters Thesis, Northeastern University. Accessed March 21, 2019. http://hdl.handle.net/2047/D20248546.

MLA Handbook (7th Edition):

Corsaro, Matthew. “Robotic grasping in cluttered scenes.” 2017. Web. 21 Mar 2019.

Vancouver:

Corsaro M. Robotic grasping in cluttered scenes. [Internet] [Masters thesis]. Northeastern University; 2017. [cited 2019 Mar 21]. Available from: http://hdl.handle.net/2047/D20248546.

Council of Science Editors:

Corsaro M. Robotic grasping in cluttered scenes. [Masters Thesis]. Northeastern University; 2017. Available from: http://hdl.handle.net/2047/D20248546


Högskolan i Halmstad

23. Uličný, Matej. Methods for Increasing Robustness of Deep Convolutional Neural Networks.

Degree: 2015, Högskolan i Halmstad

  Recent discoveries uncovered flaws in machine learning algorithms such as deep neural networks. Deep neural networks seem vulnerable to small amounts of non-random noise,… (more)

Subjects/Keywords: adversarial examples; deep neural network; noise robustness; Computer Sciences; Datavetenskap (datalogi)

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Uličný, M. (2015). Methods for Increasing Robustness of Deep Convolutional Neural Networks. (Thesis). Högskolan i Halmstad. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-29734

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Uličný, Matej. “Methods for Increasing Robustness of Deep Convolutional Neural Networks.” 2015. Thesis, Högskolan i Halmstad. Accessed March 21, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-29734.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Uličný, Matej. “Methods for Increasing Robustness of Deep Convolutional Neural Networks.” 2015. Web. 21 Mar 2019.

Vancouver:

Uličný M. Methods for Increasing Robustness of Deep Convolutional Neural Networks. [Internet] [Thesis]. Högskolan i Halmstad; 2015. [cited 2019 Mar 21]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-29734.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Uličný M. Methods for Increasing Robustness of Deep Convolutional Neural Networks. [Thesis]. Högskolan i Halmstad; 2015. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-29734

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


KTH

24. Schilling, Fabian. The Effect of Batch Normalization on Deep Convolutional Neural Networks.

Degree: CAS, 2016, KTH

Batch normalization is a recently popularized method for accelerating the training of deep feed-forward neural networks. Apart from speed improvements, the technique reportedly enables… (more)

Subjects/Keywords: batch normalization; deep learning; convolutional neural network; Computer Sciences; Datavetenskap (datalogi)

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Schilling, F. (2016). The Effect of Batch Normalization on Deep Convolutional Neural Networks. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-191222

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Schilling, Fabian. “The Effect of Batch Normalization on Deep Convolutional Neural Networks.” 2016. Thesis, KTH. Accessed March 21, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-191222.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Schilling, Fabian. “The Effect of Batch Normalization on Deep Convolutional Neural Networks.” 2016. Web. 21 Mar 2019.

Vancouver:

Schilling F. The Effect of Batch Normalization on Deep Convolutional Neural Networks. [Internet] [Thesis]. KTH; 2016. [cited 2019 Mar 21]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-191222.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Schilling F. The Effect of Batch Normalization on Deep Convolutional Neural Networks. [Thesis]. KTH; 2016. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-191222

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


KTH

25. Fristedt, Hampus. Homography Estimation using Deep Learning for Registering All-22 Football Video Frames.

Degree: Computer Science and Communication (CSC), 2017, KTH

Homography estimation is a fundamental task in many computer vision applications, but many techniques for estimation rely on complicated feature extraction pipelines. We extend… (more)

Subjects/Keywords: Deep learning; Convolutional neural network; homography; Computer Sciences; Datavetenskap (datalogi)

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APA (6th Edition):

Fristedt, H. (2017). Homography Estimation using Deep Learning for Registering All-22 Football Video Frames. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209583

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Fristedt, Hampus. “Homography Estimation using Deep Learning for Registering All-22 Football Video Frames.” 2017. Thesis, KTH. Accessed March 21, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209583.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Fristedt, Hampus. “Homography Estimation using Deep Learning for Registering All-22 Football Video Frames.” 2017. Web. 21 Mar 2019.

Vancouver:

Fristedt H. Homography Estimation using Deep Learning for Registering All-22 Football Video Frames. [Internet] [Thesis]. KTH; 2017. [cited 2019 Mar 21]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209583.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Fristedt H. Homography Estimation using Deep Learning for Registering All-22 Football Video Frames. [Thesis]. KTH; 2017. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209583

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Carnegie Mellon University

26. Yu, Zhiding. Learning Structured and Deep Representations for Traffc Scene Understanding.

Degree: 2017, Carnegie Mellon University

 Recent advances in representation learning have led to an increasing variety of vision-based approaches in traffic scene understanding. This includes general vision problems such as… (more)

Subjects/Keywords: computer vision; convolutional neural network; deep learning; scene understanding; structured prediction

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Yu, Z. (2017). Learning Structured and Deep Representations for Traffc Scene Understanding. (Thesis). Carnegie Mellon University. Retrieved from http://repository.cmu.edu/dissertations/1109

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Yu, Zhiding. “Learning Structured and Deep Representations for Traffc Scene Understanding.” 2017. Thesis, Carnegie Mellon University. Accessed March 21, 2019. http://repository.cmu.edu/dissertations/1109.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Yu, Zhiding. “Learning Structured and Deep Representations for Traffc Scene Understanding.” 2017. Web. 21 Mar 2019.

Vancouver:

Yu Z. Learning Structured and Deep Representations for Traffc Scene Understanding. [Internet] [Thesis]. Carnegie Mellon University; 2017. [cited 2019 Mar 21]. Available from: http://repository.cmu.edu/dissertations/1109.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Yu Z. Learning Structured and Deep Representations for Traffc Scene Understanding. [Thesis]. Carnegie Mellon University; 2017. Available from: http://repository.cmu.edu/dissertations/1109

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of California – Irvine

27. Jain, Akshay. Deep Learning in Chemoinformatics using Tensor Flow.

Degree: Computer Science, 2017, University of California – Irvine

 One of the widely discussed problems in the feld of chemoinformatics is the prediction of molecular properties. These properties can range from physical, chemical, or… (more)

Subjects/Keywords: Computer science; Chemistry; Chemoinformatics; Deep Learning; Recursive Neural Network; TensorFlow

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Jain, A. (2017). Deep Learning in Chemoinformatics using Tensor Flow. (Thesis). University of California – Irvine. Retrieved from http://www.escholarship.org/uc/item/963505w5

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Jain, Akshay. “Deep Learning in Chemoinformatics using Tensor Flow.” 2017. Thesis, University of California – Irvine. Accessed March 21, 2019. http://www.escholarship.org/uc/item/963505w5.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Jain, Akshay. “Deep Learning in Chemoinformatics using Tensor Flow.” 2017. Web. 21 Mar 2019.

Vancouver:

Jain A. Deep Learning in Chemoinformatics using Tensor Flow. [Internet] [Thesis]. University of California – Irvine; 2017. [cited 2019 Mar 21]. Available from: http://www.escholarship.org/uc/item/963505w5.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Jain A. Deep Learning in Chemoinformatics using Tensor Flow. [Thesis]. University of California – Irvine; 2017. Available from: http://www.escholarship.org/uc/item/963505w5

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


UCLA

28. Nakada, Masaki. Deep Learning of Neuromuscular and Sensorimotor Control with Biomimetic Perception for Realistic Biomechanical Human Animation.

Degree: Computer Science, 2017, UCLA

 We introduce a biomimetic simulation framework for investigating human perception and sensorimotor control. Our framework is unique in that it features a biomechanically simulated musculoskeletal… (more)

Subjects/Keywords: Computer science; Biomechanics; Computer Graphics; Computer Vision; Deep Learning; Neural Network

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Nakada, M. (2017). Deep Learning of Neuromuscular and Sensorimotor Control with Biomimetic Perception for Realistic Biomechanical Human Animation. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/047782rj

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Nakada, Masaki. “Deep Learning of Neuromuscular and Sensorimotor Control with Biomimetic Perception for Realistic Biomechanical Human Animation.” 2017. Thesis, UCLA. Accessed March 21, 2019. http://www.escholarship.org/uc/item/047782rj.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Nakada, Masaki. “Deep Learning of Neuromuscular and Sensorimotor Control with Biomimetic Perception for Realistic Biomechanical Human Animation.” 2017. Web. 21 Mar 2019.

Vancouver:

Nakada M. Deep Learning of Neuromuscular and Sensorimotor Control with Biomimetic Perception for Realistic Biomechanical Human Animation. [Internet] [Thesis]. UCLA; 2017. [cited 2019 Mar 21]. Available from: http://www.escholarship.org/uc/item/047782rj.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Nakada M. Deep Learning of Neuromuscular and Sensorimotor Control with Biomimetic Perception for Realistic Biomechanical Human Animation. [Thesis]. UCLA; 2017. Available from: http://www.escholarship.org/uc/item/047782rj

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


King Abdullah University of Science and Technology

29. Wang, Su. Exploring Ocean Animal Trajectory Pattern via Deep Learning.

Degree: 2016, King Abdullah University of Science and Technology

 We trained a combined deep convolutional neural network to predict seals’ age (3 categories) and gender (2 categories). The entire dataset contains 110 seals with… (more)

Subjects/Keywords: deep learning; animal trajectory; convolutional neural network; feature representation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wang, S. (2016). Exploring Ocean Animal Trajectory Pattern via Deep Learning. (Thesis). King Abdullah University of Science and Technology. Retrieved from http://hdl.handle.net/10754/610580

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Wang, Su. “Exploring Ocean Animal Trajectory Pattern via Deep Learning.” 2016. Thesis, King Abdullah University of Science and Technology. Accessed March 21, 2019. http://hdl.handle.net/10754/610580.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Wang, Su. “Exploring Ocean Animal Trajectory Pattern via Deep Learning.” 2016. Web. 21 Mar 2019.

Vancouver:

Wang S. Exploring Ocean Animal Trajectory Pattern via Deep Learning. [Internet] [Thesis]. King Abdullah University of Science and Technology; 2016. [cited 2019 Mar 21]. Available from: http://hdl.handle.net/10754/610580.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wang S. Exploring Ocean Animal Trajectory Pattern via Deep Learning. [Thesis]. King Abdullah University of Science and Technology; 2016. Available from: http://hdl.handle.net/10754/610580

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Sydney

30. Yeung, Henry Wing Fung. Object Tracking with Deep Learning and Swarm Intelligence .

Degree: 2017, University of Sydney

 Swarm Intelligence has been applied to object tracking in the recent decade. Despite the algorithm has consistently improved overtime, Swarm Intelligence based object trackers still… (more)

Subjects/Keywords: Object Tracking; Particle Swarm Optimization; Deep Learning; Convolutional Neural Network

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Yeung, H. W. F. (2017). Object Tracking with Deep Learning and Swarm Intelligence . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/16834

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Yeung, Henry Wing Fung. “Object Tracking with Deep Learning and Swarm Intelligence .” 2017. Thesis, University of Sydney. Accessed March 21, 2019. http://hdl.handle.net/2123/16834.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Yeung, Henry Wing Fung. “Object Tracking with Deep Learning and Swarm Intelligence .” 2017. Web. 21 Mar 2019.

Vancouver:

Yeung HWF. Object Tracking with Deep Learning and Swarm Intelligence . [Internet] [Thesis]. University of Sydney; 2017. [cited 2019 Mar 21]. Available from: http://hdl.handle.net/2123/16834.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Yeung HWF. Object Tracking with Deep Learning and Swarm Intelligence . [Thesis]. University of Sydney; 2017. Available from: http://hdl.handle.net/2123/16834

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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